Inverse Kinetic Parameter Optimization and Sensitivity Analysis of High-Density Polyethylene Pyrolysis

The kinetic rate constants for high-density polyethylene (HDPE) pyrolysis pose challenges due to discrepancies between experimentally determined values and those predicted by yield analysis. To address this, an inverse numerical modelling approach targeting selective product distribution was developed and evaluated using an established HDPE reaction mechanism from the literature. The objective was to achieve a mass balance target of 50 wt.% oil and 50 wt% gas within 60 minutes to minimized the squared divergence between simulated and target outputs. The ordinary differential equation solver (ode23), incorporating established pyrolysis kinetics, predicted kinetic rates for selective product distribution. The algorithm adjusted rate constants to achieve the target fit, which was validated against experimental yields at 500 °C, 550 °C, and 600 °C. Results show that inverse numerical modelling for targeted prediction of kinetic rate constants significantly improves agreement between predicted and experimental yields. Predicted rate constants indicate better performance at 500 °C, while higher temperatures exhibit more aggressive reaction behavior. The model demonstrates strong agreement with the target distribution, with R 2 = 0.999, RMSE = 0.04-0.32, and MAD = 0.04-0.18, highlighting the robustness of the proposed optimised framework. Additionally, a ±100% parametric sensitivity analysis of the optimized rate constants quantifies the pathway-specific influence of individual kinetic parameters on selective product extraction. Literature evidence further supports these findings and provides recommendations for selecting a specific kinetic rate constant and for overall process optimization. This study presents a numerical framework for accurately predicting and optimising product distributions in HDPE pyrolysis across varying temperature conditions.